SocialHelpers: Introducing social trust to ameliorate churn in P2P reputation systems

Reputation systems rely on historical information to account for uncertainty about the intention of users to cooperate. In peer-to-peer (P2P) systems, however, accumulating experience tends to be slow due to the high rates of churn - the continuous process of arrival and departure of peers. The flow of transactions is continuously interrupted by departures, which can significantly affect the convergence of reputation systems. To shed light on this, this paper presents an accurate model for capturing the influence of churn on the process of building reputations. Using our model, system architects can determine the minimal transaction rate that guarantees fast convergence and design their systems accordingly. Unfortunately, the natural transaction rate of users is sometimes too low (e.g., due to physical constraints like network bandwidth, etc.) that many of them are likely to experience significant delays in the process of building reputations for their neighbors. We face this problem by leveraging the inherent trust in social networks. The basic idea is that users ask their social links to transact with strangers and together generate reputation ratings in a short time scale. Our simulation results report reductions of 50% or greater in the convergence time in environments with high churn rates.